Search Results (9)

One of the primary steps in managing the flow in an open channel is determining its properties. Empirical equations are developed to provide further information regarding the flow in open channels. Obtaining such experimental equations is expensive and time consuming; therefore, alternative solutions have been sought. Over the last century, the Parshall flume, a static measuring device with no moving parts, has played a significant role in measuring the flow in open channels. Many researchers have focused their interest on studying the application of Parshall flumes in various fields like irrigation and wastewater management. Although various scholars used experimental results to enhance the rating equation of the Parshall flume, others used an alternative source of data to recalibrate the height–discharge relation equation using numerical simulation. Computational Fluid Dynamic (CFD) software is becoming popular nowadays as computing hardware has advanced significantly within the last few decades, making it possible to go beyond the limited resolution that was experienced in the past. Multiple CFD models, depending on their availability, either open-source or commercially licensed, have been used to perform numerical simulations on different configurations of flumes, especially Parshall flumes, to produce water level results. Regarding various CFD tools that have been used, i.e., FLOW-3D, Ansys Fluent, or OpenFOAM, after precise calibration with experimental data, it has been determined that the output is reliable and can be implemented to the actual scenarios. The benefit of using this technique to produce results is the ability of the CFD approach to adjust the initial conditions, like flow velocity or structural geometry, where necessary. With respect to channel size and the condition of the site where the flume is located, the choices are narrowed to the specific Parshall flume suitable to the situation. It is not always possible to select the standard Parshall flume; therefore, engineers provide some modification to the closest flume size and provide a new rating curve to produce accurate flowrates. This review has been performed on the works of a number of scholars who targeted the application of numerical simulation and physical experimental data in Parshall flumes to either enhance the existing rating equation or propose further modification to the structure’s geometry. Full article

This study uses a computational fluid dynamics (CFD) approach to simulate flows in Parshall flumes, which are used to measure flowrates in channels. The numerical results are compared with the experimental data, which show that choosing the right turbulence model, e.g., $v^2-f$ and LC, is the key element in accurately simulating Parshall flumes. The Standard Error of Estimate (SEE) values were very low, i.e., 0.76% and 1.00%, respectively, for the two models mentioned above. The Parshall flume used for this experiment is a good example of a hydraulic structure for which the design can be more improved by implementing a CFD approach compared with a laboratory (physical) modeling approach, which is often costly and time-consuming. Full article

In order to measure flow rate in open channels, including irrigation channels, hydraulic structures are used with a relatively high degree of reliance. Venturi flumes are among the most common and efficient type, and they can measure discharge using only the water level at a specific point within the converging section and an empirical discharge relationship. There have been a limited number of attempts to simulate a venturi flume using computational fluid dynamics (CFD) tools to improve the accuracy of the readings and empirical formula. In this study, simulations on different flumes were carried out using a total of seven different models, including the standard k–ε, RNG k–ε, realizable k–ε, k–ω, and k–ω SST models. Furthermore, large-eddy simulation (LES) and detached eddy simulation (DES) were performed. Comparison of the simulated results with physical test data shows that among the turbulence models, the k–ε model provides the most accurate results, followed by the dynamic k LES model when compared to the physical experimental data. The overall margin of error was around 2–3%, meaning that the simulation model can be reliably used to estimate the discharge in the channel. In different cross-sections within the flume, the k–ε model provides the lowest percentage of error, i.e., 1.93%. This shows that the water surface data are well calculated by the model, as the water surface profiles also follow the same vertical curvilinear path as the experimental data. Full article

This article deals with the possibility of using a biosorbent in the form of a mixture of cones from coniferous trees to remove the residual concentration of hazardous metals contained in hazardous waste, which is disposed of in a neutralization station. The efficiency of the tested biosorbent in removing Ni, Zn, Cu, and Fe was monitored here. Laboratory research was carried out before the actual testing of the biosorbent directly in the operation of the neutralization station. With regard to the planned use of the biosorbent in the operational test, the laboratory experiments were performed in a batch mode and for the most problematic metals (Ni and Zn). The laboratory tests with real wastewater have shown that the biosorbent can be used to remove hazardous metals. Under the given conditions, 96% of Ni and 19% of Zn were removed after 20 min when using NaOH activated biosorbent with the concentration of 0.1 mol L⁻¹. The inactivated biosorbent removed 93% of Ni and 31% of Zn. The tested biosorbent was also successful during the operational tests. The inactivated biosorbent was applied due to the financial costs. It was used for the pre-treatment of hazardous waste in a preparation tank, where a significant reduction in the concentration of hazardous metals occurred, but the values of Ni, Cu, and Zn still failed to meet the emission limits. After 72 h, we measured 10 mg L⁻¹ from the original 4,056 mg L⁻¹ of Ni, 1 mg L⁻¹ from the original 2,252 mg L⁻¹ of Cu, 1 mg L⁻¹ from the original 4,020 mg L^–1 of Zn, and 7 mg L⁻¹ from the original 1,853 mg L⁻¹ of Fe. However, even after neutralization, the treated water did not meet the emission limits for discharging into the sewer system. The biosorbent was, therefore, used in the filtration unit as well, which was placed in front of the Parshall flume. After passing through the filtration unit, the concentrations of all the monitored parameters were reduced to a minimum, and the values met the prescribed emission limits. The biosorbent was further used to thicken the residual sludge in the waste pre-treatment tank, which contributed to a significant reduction in the overall cost of disposing of residual hazardous waste. This waste was converted from liquid to solid-state. Full article

Only a few scientific research studies referencing extremely low flow conditions have been conducted in Greece so far. Forecasting future low stream flow rate values is a crucial and decisive task when conducting drought and watershed management plans by designing construction plans dealing with water reservoirs and general hydraulic works capacity, by calculating hydrological and drought low flow indices, and by separating groundwater base flow and storm flow of storm hydrographs, etc. The Artificial Neural Network modeling simulation method generates artificial time series of simulated values of a random (hydrological in this specific case) variable. The present study produces artificial low stream flow time series of part of 2015. We compiled an Artificial Neural Network to simulate low stream flow rate data, acquired at a certain location of the entirely regulated, urban stream, which crosses the roads junction formed by Iokastis road and an Chrisostomou Smirnis road, Agios Loukas residential area, Kavala city, Eastern Macedonia & Thrace Prefecture, NE Greece, during part of July, August, and part of September 2015, until 12 September 2015, using a 3-inches conventional portable Parshall flume. The observed data were plotted against the predicted one and the results were demonstrated through interactive tables by providing us the ability to effectively evaluate the ANN model simulation procedure performance. Finally, we plotted the recorded against the simulated low stream flow rate data by compiling a log-log scale chart, which provides a better visualization of the discrepancy ratio statistical performance metrics and calculated further statistic values featuring the comparison between the recorded and the forecasted low stream flow rate data. Full article

Drainage from the 27,316-m² Jacob K. Javits Convention Center (JJCC) green roof was investigated in the field to quantify the system’s long-term rainfall-runoff response. The JJCC hosts one of the largest extensive green roofs in the United States. Utilizing four years of rooftop monitoring data collected using a weather station, custom designed and built drainage systems, three Parshall flumes equipped with pressure transducers, and weighing lysimeters, this study quantified the 25.4-mm-deep green roof’s ability to decrease the volume and peak rate of runoff. With parameters derived from the site, the Environmental Protection Agency Stormwater Management Model (EPA-SWMM) predicted event total runoff volume and event peak runoff rates to within +10% to −20% and +25% to −15% of the observations, respectively. The analysis further indicated that approximately 55% of the cumulative precipitation that fell on the JJCC extensive green roof during the monitoring period (warm weather months, June 2014–November 2017) was captured and retained. The average percent retained on an event-basis was 77%, and average event runoff coefficient was 0.7, implying a substantial reduction in the volume and rate of runoff generated from the roof compared to the pre-green roof condition, when most, if not all, of the precipitated water would have immediately resulted in runoff. Our research suggests that, on average, 96% of rainfall events 6.35 mm or less were retained within the green roof, whereas 27% of the total event volume was retained for events greater than 12.7 mm in depth. A sensitivity analysis suggests if the substrate depth were increased, better stormwater capture performance would be achieved, but only up 127 mm, whereas increased precipitation coupled with warmer temperatures as a result of climate change could decrease the performance by up to 5%, regardless of substrate depth. An equivalency analysis suggested that even shallow green roofs can significantly reduce the required stormwater detention volume that New York City requires on new development. This particular green roof appears to be more than 18 times as cost-effective as a subsurface cistern would be for managing an equivalent volume of stormwater in Midtown Manhattan. Full article

Only a few scientific research studies, especially dealing with extremely low flow conditions, have been compiled so far, in Greece. The present study, aiming to contribute in this specific area of hydrologic investigation, generates synthetic low stream flow time series of an entire calendar year considering the stream flow data recorded during a center interval period of the year 2015. We examined the goodness of fit tests of eleven theoretical probability distributions to daily low stream flow data acquired at a certain location of the absolutely channelized urban stream which crosses the roads junction formed by Iokastis road an Chrisostomou Smirnis road, Agios Loukas residential area, Kavala city, NE Greece, using a 3-inches conventional portable Parshall flume and calculated the corresponding probability distributions parameters. The Kolmogorov-Smirnov, Anderson-Darling and Chi-Squared, GOF tests were employed to show how well the probability distributions fitted the recorded data and the results were demonstrated through interactive tables providing us the ability to effectively decide which model best fits the observed data. Finally, the observed against the calculated low flow data are plotted, compiling a log-log scale chart and calculate statistics featuring the comparison between the recorded and the forecasted low flow data. Full article

A small number of scientific research studies with reference to extremely low flow conditions, have been conducted in Greece, so far. Predicting future low stream flow rate values is an essential and of paramount importance task when compiling watershed and drought management plans, designing water reservoirs and general hydraulic works capacity, calculating hydrological and drought low flow values, separating groundwater base flow and storm flow of storm hydrographs etc. The Monte-Carlo simulation method generates multiple attempts to define the anticipated value of a random (hydrological in this specific case) variable. The present study compiles, correspondingly, artificial low stream flow time series of both the same part of the year (2016) as well as a part of the calendar year (2017), based on the stream flow data observed during the same two different interval periods of the years 2016 and 2017, using a 3-inches U.S.G.S. modified portable Parshall flume, a 3-inches conventional portable Parshall flume, a 3-inches portable Montana (short Parshall) flume and a 90° V-notched triangular shaped sharp crested portable weir plate. The recorded data were plotted against the fitted one and the results were demonstrated through interactive tables providing us the ability to effectively evaluate the simulation procedure performance. Finally, we plot the observed against the calculated low stream flow rate data, compiling a log-log scale chart which provides a better visualization of the discrepancy ratio statistical performance metric and calculate statistics featuring the comparison between the recorded and the forecasted low stream flow rate data. Full article

Only a few scientific research studies with reference to extremely low stream flow conditions, have been conducted in Greece, so far. Forecasting future low stream flow rate values is a crucial and desicive task when conducting drought and watershed management plans, designing water reservoirs and general hydraulic works capacity, calculating hydrological and drought low flow indices, separating groundwater base flow and storm flow of storm hydrographs etc. Artificial Neural Network modeling simulation method generates artificial time series of simulated values of a random (hydrological in this specific case) variable. The present study produces artificial low stream flow time series of both a part of the past year (2016) as well as the present year (2017) considering the stream flow data observed during two different respecting interval period of the years 2016 and 2017. We compiled an Artificial Neural Network to simulate low stream flow rate data, acquired at a certain location of the partly regulated semi-urban stream which runs through the eastern exit of Kavala city, NE Greece, using a 3-inches U.S.G.S. modified portable Parshall flume, a 3-inches conventional portable Parshall flume, a 3-inches portable Montana (short Parshall) flume and a 90° V-notched triangular shaped sharp crested portable weir plate. The observed data were plotted against the predicted one and the results were demonstrated through interactive tables providing us the ability to effectively evaluate the ANN model simulation procedure performance. Finally, we plot the recorded against the simulated low stream flow rate data, compiling a log-log scale chart which provides a better visualization of the discrepancy ratio statistical performance metrics and calculate the derived model statistics featuring the comparison between the recorded and the forecasted low stream flow rate data. Full article